Data Visualization Framework

1. Understand the audience: The format of the visualization should be easily assimilated by the consumers of the information -decision-makers. The audience should be able to read and interpret the information. The viewer’s expectations and the type of information which is most useful to them should be taken into consideration.
2. Big-picture considerations: The Visualization should include appropriate amount of data so that it is easy to understand. Too much of data may overload the reader with too much of information. The visualizations with a little amount of data might undermine the purpose of creating the Visualization and might not convey the required information to the user/reader. The data visualization should contain proper metadata to help user understand the information which is being conveyed.
3. Color: Choosing the complementary colors have a favorable influence on the viewer’s eyes. But the colors should be used consistently so that the viewers are not confused.
4. Text formatting: The choice of proper and meaningful words is very important. Each word should be made useful and understandable while limiting the text used in the Visualization.
5. Patterns: Patterns help us to predit what happens next and also helps to develop the ability to make predictions and prepare for the future.A good data visualization splices the data many ways, looking for patterns that emerge over time, across space or between categories.Exploring the data in this way allows significant patterns to emerge - these patterns tell a story about how the world works or is changing.
6. Consistent Scales: When representing multiple variables in a single data visualization,it is always a good practice to represent them on a single scale to avoid confusion.
7. Visually appealing design: A good data visualization appeals the audience and it is aesthetically pleasing.
8. Graphical Integrity: Graphical integrity refers to how accurately the visual elements represent the data. On the surface, this would seem to be straight forward. But in fact, information can vary widely, even for data that is related. So there is a desire and tendency to scale the data disproportionately in order to make it fit in the space allowed.Or when values are tightly packed in one area, and sparse in another, there is a desire and tendency to spread things out evenly. In each case, this can lead to a false impression of the data, and incorrect conclusions.

References:

Data Viz1 Data Viz2 Data Viz3 Data Viz4 Data Viz5

The visualizations depicting the climate change caused by human activities

1.Climate changes due to global warming 1

Climate risks comparison due Global Warming

Evaluation based on Data Visualization Framework 1.Understand the audience It is easy to read and interprete the information.Impact of the climate change due to global warming on various entities like weather,water availability,people,species,sea-level rise,oceans is clearly mentioned 2.Big-picture considerations The visualization coneys proper amount of data and it doesn’t overload the reader with too much of data. Clear members are mentioned for many entities like weather,species,water availability etc but they are missing for the entities like oceans,cost and food. 3.Color The visualization uses complementary colors so that it doesn’t look flamboyant. 4.Text formatting The text used for representing the data could have been minimized. Just putting the numbers with the help of some symbols for increase and decrease could have been sufficient instead of writing the sentences for each entity. 5.Patterns As the entities shown in the visualization are not closely related to each other, there is no pattern shown relating them. 6.Consistent Scales The scales used to show increase or decrease are consistent throughout the visualization 7.Visually appealing design The visalization seems visually appealing to the layman. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.

2.Vulnerability to climate change due to Carbon dioxide emission 2

Evaluation based on Data Visualization Framework 1.Understand the audience It is not easy to read and interprete the information for a common man.Prior knowledge of the dataset is required before interpreting the visualization correctly. 2.Big-picture considerations The name of the countries are put in the circular fashion hence it is quite difficult to read those names. But this approach might not cause a problem for an expert reader. 3.Color The visualization uses complementary colors so that it doesn’t look too fancy. 4.Text formatting The amount of text used for representing the data is sufficient. 5.Patterns Increasing or decreasing circular patterns for each nation effectively represents CO2 emitting nations and vulnerable nations. 6.Consistent Scales The scales are consistent throughout the visualization 7.Visually appealing design The visalization doesn’t seems visually appealing to the layman.But it might be easily understood by an expert who is equipped with the prior knowledge of the data. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.

3.Climate change due to greenhouse effect 3

The role of the greenhouse effect on the temperature change

Evaluation based on Data Visualization Framework
1.Understand the audience It is not easy to relate and interprete the information shown in the two graphs. 2.Big-picture considerations The data represented is too statistical and not easily readable. 3.Color The visualization uses decent colors to represent the two graphs. 4.Text formatting The amount of text used in the visualization is very limited. 5.Patterns Patterns are not applicable for this type of visualization using graphs. 6.Consistent Scales The scales are consistent throughout the visualization 7.Visually appealing design The visalization doesn’t seems visually appealing to the layman.But it might be easily understood by an expert who is equipped with the prior knowledge of the data. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.

4.Climate change versus % change in the population 4

Climate change vulnerability for the fastest growing african cities)

Evaluation based on Data Visualization Framework 1.Understand the audience It is not easy to understand the visualization.Prior knowledge of the dataset is necessary. 2.Big-picture considerations The definition of Climate change vulnerability index should be specified somewhere, else it is very difficult to interprete the information. 3.Color The visualization uses dark colors for each continent. The significance of the background color is not specified/easily understood. 4.Text formatting The amount of text used in the visualization is limited.
5.Patterns The circular patterns vary in sizes and it is difficult to understand the difference between the two circles with almost similar sizes. 6.Consistent Scales The scales are consistent throughout the visualization 7.Visually appealing design The visalization doesn’t seems visually appealing to the layman as information is cluttered throughout the visualization. But it might be easily understood by an expert who is equipped with the prior knowledge of the data. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.

The data visualization showing the climate change caused by Sun’s energy

5.Climate changes due to Sun’s energy 5

Climate change due to Sun’s energy

Evaluation based on Data Visualization Framework 1.Understand the audience It is not easy to understand the visualization.Prior knowledge of the dataset is necessary. 2.Big-picture considerations Too much information is represented in a small area.The use of bar charts would have been more useful as bar charts are easy to read. 3.Color The visualization uses decent colors to represent the two graphs. 4.Text formatting The amount of text used in the visualization is limited.
5.Patterns Patterns are not applicable for this type of visualization using graphs. 6.Consistent Scales The scales are consistent throughout the visualization 7.Visually appealing design The visalization doesn’t seems visually appealing to the layman as information is not easily readable. But it might be easily understood by an expert who is equipped with the prior knowledge of the data. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.

Overall assessment and conclusion

After completing the literature survey for effective evaluation of data visualizations, 'Data Visualization Framework has been designed. The framework consists of 8 key criteria which are very critical in evaluating the data visualizations. Out of the total five visualizations evaluated, none of the visualizations seems perfect matching all the eight criteria.The exercise of designing a data visualization framework is really helpful and led us to conduct deep research and meticulous analysis of the existing data visualizations.